MASTER'S THESIS
Available Bandwidth Measurement in 4G Networks
Saqlain Haider Harpal Singh
2013
Master of Science (120 credits) Computer Science and Engineering
Luleå University of Technology
Department of Computer Science, Electrical and Space Engineering
Available Bandwidth Measurement in 4G Networks
Author
Harpal Singh Saqlain Haider
Masters in Mobile systems
Department of Computer Science, Electrical and Space Engineering Luleå University of Technology
Luleå, Sweden
Supervisors
Anders Hedlund
Ascom Network Testing AB www.ascom.com
Christer Åhlund Luleå University of Technology www.ltu.se
Preface
This Master thesis work has been done as a partial fulfilment of Master degree in Computer Science, Electrical and Space Engineering with specialization in the field of Mobile Systems at Luleå University of Technology, Luleå, Sweden.
The work was carried out at Terminal and Pocket Division of Ascom Network Testing AB, Skellefteå, Sweden. Ascom Network Testing AB provide solutions for drive testing, analysing, benchmarking and monitoring of mobile network performance. Ascom Network Testing AB’s continuous R & D contributes with a market share of 40 % in the field of drive testing for wireless communication networks. Ascom develops technology for deploying, monitoring and assurance of quality of service for broadband wireless networks.
Acknowledgement
We would like to thank our mentor and external advisor Anders Hedlund (Se- nior Specialist User Equipment Measurements) at Ascom Network Testing AB, Skellefteå, Sweden for his support, motivation, valuable comments and time he invested in this project which allowed us to gain a deeper understanding in the field of network performance measurements. We got an opportunity to go beyond the academic and research work to make this a meaningful personal and professional experience.
We would like to extend our gratitude to Adrain Jakobsson (Department Man- ager R & D,Terminals & Pockets) at Ascom Network Testing AB, who provided us a platform to conduct our research. He has been a dedicated manager with instant response to queries. We would like to acknowledge him for allowing us to be a part of ongoing research and development in Terminal and pocket division.
Special thanks to Prof. Christer Åhlund (Program co-ordinator, Masters in Mobile Systems) from Department of Computer Science, Electrical and Space Engineering of Luleå University of Technology, Sweden for his supervision to provide valuable comments and suggestions. He has guided us by the means of courses offered by the division and provided a strong foundation in the field of wireless communication. We are grateful for both our supervisors for sparing valuable time for guidance and thesis report review with meaningful comments.
We are grateful to our family members, close friends and colleagues from Luleå University of Technology, for their encouragement and support. At last we would be thankful to all employees of Ascom Network Testing AB for providing a friendly and supportive environment throughout the research period.
Luleå, October 2012 Harpal Singh
Saqlain Haider
Abstract
Existing available bandwidth estimation tools were mainly designed for fixed IP backbone networks and desktops with assumption of First In First Out(FIFO) Principle. While LTE supports high data rates of 100 Mbps, challenge is to adapt available bandwidth algorithms for quick and non-intrusive measure- ments on an Android OS based device supporting different MIMO configu- rations. This Master thesis presents real time, two way available bandwidth measurement tool using Two Way Active Measurement Protocol (TWAMP) for Android OS based devices to measure high speed LTE networks. The tool performance is verified against FTP throughput measurement with effect of variation in constant bit rate UDP cross traffic and load on the server on end- to-end measurements. The results from emulation and tests in a commercial LTE network show that we can achieve available bandwidth estimations on both the uplink and downlink in real time. This opens up wide possibility to include various existing available bandwidth techniques and tools in mobile application to be used over a wireless link. Further, possible suggestions to achieve better available bandwidth estimations with native application devel- opment has been proposed.
Contents
Preface i
Acknowledgement ii
Abstract iii
List of Figures & Tables vi
List of Abbreviations viii
1 Introduction 1
1.1 Bandwidth Related Metrics . . . 3
1.1.1 Capacity . . . 3
1.1.2 Available Bandwidth . . . 3
1.1.3 Bulk Transfer Capacity (BTC) and TCP throughput . . . . 4
1.2 Background . . . 5
1.3 Related Work . . . 7
1.4 Thesis Objective . . . 8
1.5 Research Methodology . . . 9
1.6 Individual Contribution . . . 10
1.7 Report Outline . . . 10
2 Study Review 11 2.1 Available bandwidth estimation tools and techniques . . . 11
2.1.1 Probe Gap Model . . . 11
2.1.2 Probe Rate Model . . . 14
2.1.3 Performance metrics . . . 16
2.2 LTE Overview . . . 17
2.2.1 LTE Radio Interface Architecture . . . 19
2.2.2 QoS Control in LTE . . . 20
2.3 Android OS . . . 25
2.3.1 Android supported API’s . . . 26
2.4 Measurement errors for available bandwidth estimation . . . 30
Contents
3 Two Way Measurement 32
3.1 Two Way Active Measurement Protocol [TWAMP] . . . 32
3.1.1 Logical Model: . . . 33
3.1.2 TWAMP Control: . . . 34
3.1.3 TWAMP Test: . . . 34
3.2 TWAMP Implementation: . . . 34
3.3 TWAMP Light . . . 35
4 Architecture Implementation 36 4.1 Test Bed . . . 36
4.1.1 TWAMP Client: . . . 36
4.1.2 TWAMP Server . . . 37
4.1.3 Cross traffic generator: . . . 37
4.1.4 Multiple client Emulator . . . 37
4.2 Evaluation . . . 38
5 Results 40 6 Conclusion and Future work 43 Bibliography 45 A Format of Test Packet 50 A.1 Test packet from the Session Sender . . . 50
A.2 Test packet from the Session Reflector . . . 51
List of Figures & Tables
1.1 Ericsson: Traffic and Market Report June 2012 . . . 2
1.2 End-to-end communication path . . . 2
1.3 Narrow and Tight links in Multi-hop network . . . 3
1.4 Available bandwidth in an average timescale period . . . 4
1.5 Available Bandwidth Estimation . . . 6
2.1 PGM Model . . . 12
2.2 PRM model . . . 14
2.3 Stream Variation with One Way Delay . . . 16
2.4 Pathchirp train structure . . . 16
2.5 LTE frame structure . . . 18
2.6 The bearer and its associated QoS parameters . . . 22
2.7 Android OS Layer . . . 25
2.8 Android SDK flow . . . 27
2.9 Android NDK flow . . . 27
2.10 Android Renderscript Flow . . . 29
2.11 Android JNI . . . 29
3.1 TWAMP logic model . . . 33
3.2 TWAMP Light model . . . 35
4.1 TWAMP implementation with cross traffic . . . 36
4.2 TWAMP implementation with load on server . . . 38
5.1 Available bandwidth and FTP results with cross traffic in Mbps . 40 5.2 Available bandwidth estimation with load on server . . . 41
A.1 Test packet from the Session Sender . . . 50
A.2 Test packet from the Session Reflector . . . 51
List of Abbreviations
AMBR Aggregate Maximum Bit Rate APN Access Point Name
ARP Allocation and Retention Priority BART Bandwidth Available in Real Time BTC Bulk Transfer Capacity
CQI Channel Quality Indicator
DSCP Differentiated Services Code Point EPC Evolved Packet Core
FDD Frequency Division Duplexing GBR Guaranteed Bit Rate
IGI Initial Gap Increasing IMS IP Multimedia Subsystem LTE Long Term Evolution MAC Medium Access control MBR Maximum Bit Rate
MCS Modulation and Coding Scheme MME Mobility Management Entity
OFDM Orthogonal Frequency Division Multiplexing OFDMA Orthogonal Frequency Division Multiple Access PCC Policy and Charging Control
PCRF Policy and Charging Rules Function PDCP Packet Data Convergence Protocol PDN Packet Data Network
PGM Probe Gap Model
PMI Pre-coder Matrix Indicator PPTD Packet Pair/ Train Dispersion PRB Physical Resource Block PRM Probe Rate Model
PTR Packet Transmission Rate QoS Quality of Service
RAN Radio Access Network RI Rank Indicator
RLC Radio Link Control
List of Figures & Tables
RRC Radio Resource Control
RSRP Reference Signal Received Power SPRUCE Spread Pair Unused Capacity Estimate TCP Transmission Control Protocol
TDD Time Division Duplexing TOPP Train of Packet Pairs
TSG Technical Specifications Group
TWAMP Two Way Active Measurement Protocol UDP User Datagram Protocol
VPS Variable Packet Size
3GPP Third Generation Partnership Project
Chapter 1
Introduction
Long Term Evolution (LTE) also termed as 4G was introduced in 3GPP re- lease 8 [2]. This release provides a significant development over existing 3G UMTS/HSPA networks. The core development in LTE is the use of OFDM based air interface and flat IP based network architecture for voice and data traffic. Higher order modulation, larger bandwidth utilization and MIMO us- age in LTE allows it to achieve asymmetric data rates of up to 100 Mbps for downlink and 50 Mbps for uplink in user traffic. Support for both Time Division Duplexing (TDD) and Frequency Division Duplexing (FDD) is pro- vided and its usage is operator implementation specific. TDD has restrictions for providing higher data rates due to scheduling pattern which gives prefer- ence for FDD. Downlink radio link has been optimized for higher throughput performance whereas the uplink is modified for better power management con- sidering end-user device perspective.
There would be 5 billion mobile broadband subscriptions by end of year 2017 as per predictions made by Ericsson [20] regarding the growth rate as illustrated in figure 1.1. The growth of smart phones usage will increase from 700 million (2011) to 3 billion (2017). As these devices gets under affordable price ranges, we will see large penetration in its usage and subscription to mobile internet usage. The driving force has been the development of dedicated hardware, efficient mobile operating system and affordable prices for the smart phones supporting high speed LTE networks.
Available bandwidth estimation is crucial for traffic engineering, QoS manage- ment, multimedia streaming, server selection in application services, conges- tion management and network capacity provisioning in wireless mobile net- works. Available bandwidth measurement can be considered essential to en- sure that the wireless mobile operators are living up to the standard of the quality of service guaranteed by them while providing desired date rates to the users. This can also be considered to compare the performance index of various Telecom operators in a specific region. Various projects have been de- veloped by time to measure the network performance metrics by looking into various characteristics of the network. These studies involved in investigating
Figure 1.1: Ericsson: Traffic and Market Report June 2012
the traffic modelling and characterization in a network and the network topol- ogy for the end-to-end measurements. These key studies had lead researchers to adopt these defined characteristics to develop new protocols and simulation parameters.
Figure 1.2: End-to-end communication path
1.1. Bandwidth Related Metrics
1.1 Bandwidth Related Metrics
As shown in figure 1.2, an end-to-end communication path is a single route that connects two end hosts through a set of communication links or hops connected via network devices. In order to define bandwidth metrics for an end-to-end path such as Capacity, Available bandwidth and Bulk Transfer Ca- pacity (BTC), we need to consider an end-to-end communication path.
1.1.1 Capacity
For N links in a certain end-to-end path, capacity of a path is the maximum possible IP layer rate the path can transfer from source to receiver. The capac- ity of the link depends on the underlying transmission technology and prop- agation medium. The following definitions related to capacity are discussed below and shown in figure 1.3.
Figure 1.3: Narrow and Tight links in Multi-hop network
Link Capacity: Link capacity of a specified link is defined as maximum amount of bits that can be transmitted per unit time.
Tight Link: The link with the minimum available bandwidth can be defined as the tight link of the path. Available bandwidth is discussed in section 1.1.2.
Narrow Link: The link with minimum capacity which sets the upper bound for the capacity of the whole network is called the narrow link of the path.
1.1.2 Available Bandwidth
End-to-end available bandwidth is a time varying metric ( Figure 1.4) which can be defined as the minimum of all non-utilized link capacities throughout the communication path for that given time interval. We can define t as
1.1. Bandwidth Related Metrics
averaging time-scale over which the available bandwidth estimate is provided.
The available bandwidth of a link depends on the underlying transmission technology, propagation medium and traffic load on that link.
Figure 1.4: Available bandwidth in an average timescale period
1.1.3 Bulk Transfer Capacity (BTC) and TCP through- put
A throughput of a TCP connection is essential but various factors affect TCP throughput such as transfer size, parallel cross traffic (UDP or TCP), number of established TCP connections, TCP socket buffer size at both ends or pos- sibility of congestion along acknowledgement on reverse path. Further, router buffer size, load and capacity of each link on the end-to-end path, initial win- dow size also affects the TCP throughput measurements.
Bulk Transfer Capacity is defined as the maximum throughput which can be obtained by a single TCP connection. It is different as compared to the avail- able bandwidth measurement, as available bandwidth measurement does not depend on specific transport protocol whereas it usually uses UDP. The results for BTC measurements depend on how TCP shares the bandwidth with other TCP flows. In case of available bandwidth measurement it assumes the cross traffic to be constant and estimates the spare capacity on the link.
Capacity and available bandwidth estimation is done either for individual hops or for end-to-end path. Variable Packet Size (VPS) probing techniques defined in tools such as Pathchar [28] and Pchar [37]estimates capacity of individual hops whereas tools such as CapProbe [32], nettimer [34], pathrate [17], sprobe [48] come under Packet Pair/Train Dispersion (PPTD) technique. Tools such as Pathload [29], IGI [24], pathChirp [45] measures end-to-end available band- width. TReno [38],Cap [6] comes under BTC whereas achievable throughput can be calculated by TTCP [54], Iperf [53], NetPerf [31].
1.2. Background
Measuring end-to-end available bandwidth in a LTE network can be a challeng- ing task due to varying wireless channel conditions, scheduling & modulation techniques, pre-configured QoS parameters as well as requirement of dedicated hardware and underlying OS at both the end measurement points.The limita- tions to accurate network performance measurements may be the air interface as well as transport network. Considering the scare wireless resources it is essential for available bandwidth estimation tools & techniques to be quick &
least intrusive.
1.2 Background
Available bandwidth measurement techniques can be classified into two broad categories. One being passive estimation and the other being active probing.
Passive estimation is done on the basic of congestion situation, packet loss and delay performance to estimate the available bandwidth. Active probing on the other hand sends probe packets over a network to estimate the available bandwidth. Due to efficiency and the reliability of estimations, active probing is usually considered. Active probing further consists of probe gap or probe rate models.
The probe gap model generates the estimate of available bandwidth by estima- tion of cross traffic rate in the link. Tools developed following this technique require the previous knowledge of the capacity of the network path to be mea- sured. The working behind this probing technique is where the sender sends pair of packets to the receiver. The pair packets are transmitted close enough together in time for packets to queue together at bottleneck link. Measuring the change in packet spacing, the receiver can make an estimate of amount of cross traffic during the measurement time in the link.
The probe rate techniques operate on a basis of self induced congestion where this mechanism sends stream of packets where the input rate of each stream is varied either iteratively or exponentially. Here, the available bandwidth is defined as the lowest input rate which overloads the network. The avail- able bandwidth estimation in probe rate model does not consider the previous knowledge of tight link capacity in the network. Each technique under probe rate model or probe gap model has different probing techniques, levels of in- trusiveness and estimation time.
Active probing techniques for both probe gap and probe rate models can be classified into two phases to provide estimates of available bandwidth of the network. The first phase defines sending probe packets in a selected man- ner. To measure end-to-end available bandwidth we need to inject probing packets in to the network so that we could sample it for that specific time.
The motive behind sending probing packets in selective manner is to not add massive additional traffic on the network to be measured and get close to the measurement of tight link. Any probe rate higher than the tight link capacity shows delays within the probing packets crossing through that tight link. This
1.2. Background
suffered delay within the probing packets assists us to calculate the available bandwidth.
There are various probing schemes existing. Initial ones consider probe pack- ets arranged in multiple packet pairs as shown in 1.5 . This probing sequence is defined by two parameters. One of them is Intra pair interval defined as Din and the inter Pair interval t. Train Of Packet Pairs(TOPP) follows the concept of different parameters of Din. Thirdly is to send train of N pack- ets with varying probe rates and gap within each consecutive probe train.
Fourthly is the usage of sequence of exponentially spaced packets defined as
"Chirps"(Figure 2.4) where the train of packets is sent with increasing probing rate within two consecutive probing packets.
These inter packet pair time separation may be negative, positive or zero as shown in Figure 1.5. A negative value (Dout < Din) occurs when the first packet gets cross traffic packets in the queue followed by the second packet whereas a positive value (Dout > Din) occurs when the cross traffic packets gets inserted between the probing packets pair in the queue. (Dout = Din) occurs when cross traffic has no effect on the inter packet separation.
Figure 1.5: Available Bandwidth Estimation
Further the second phase defines the analysis phase where the received probe packets are analysed to estimate available bandwidth. The analysis is further divided into 2 parts. It is either done by one way delay calculations of probe packet train or packet dispersion of probing packets pairs. Various factors add noise to the estimation which will be discussed later in chapter 2. To reduce the factors adding some noise to the estimation, filters such as exponentially weighted average, intersection filters or kalman filters have been used in some of the tools.
Most of the available bandwidth measurement tools and techniques consider available bandwidth measurement in one direction.To measure it on both up- link and downlink, they need to be deployed on both of the end points with an complex architecture with each end serving as client as well as server. As the available bandwidth of uplink and downlink links are asymmetric, the conventional way of sending packets in one way and then initiating a session
1.3. Related Work
on the other link without the knowledge of probe rate does not seem to be a appropriate choice. This provides a complex architecture for the measure- ment where the server also initiates a session towards the client for downlink measurement providing a load on the server where it needs to handle various clients.We further may need root access to the server to initiate a session. To address this issue to analyse both the links on real time we propose a mech- anism to estimate available bandwidth using Two Way Active Measurement Protocol(TWAMP).
1.3 Related Work
The concept of ICMP ECHO to probe wired IP network paths for its bot- tleneck link capacity and available bandwidth for round trip paths has been proposed [14]. However it was not possible to detect the bottleneck link to be either on the uplink or downlink path. Further, ICMP timestamps and Traceroute concepts to estimate capacity and cross traffic volume of each link of network path have also been utilized by some authors [47]. However, the authors did not consider the existence of cross traffic in the link. Also, the as- sumption of intermediate nodes along the uplink path and downlink path were considered the same which are not feasible on real networks. Trace route allow us to find the intermediate nodes from the source to server destination. Modi- fied ping program was proposed with an application of packet trains to measure available bandwidth with a developed tool named GNAPP [35]. It can iden- tify several tight links along the path with individual available bandwidth. It utilizes stage filtering and moving averages to provide better estimations. The results calculated by the author show good accuracy under bursty traffic with multiple tight links on the path.
It is essential to understand the properties and behaviours of end-to-end paths.
There lies a possibility of congestion in multiple links with probing packets by all available bandwidth estimation tools and techniques using self-induced congestion. Hence, individual available bandwidth for all congested links need to be evaluated to estimate the available bandwidth of the network path. Some tools such as Delphi [44], IGI/PTR [24], Pathload [29], and PathMon [33]
only estimate a network path with single tight link. MoSeab [57], TOPP [39], PathChirp [45] can be applied to path with multiple tight link. MoSeab estimates the available bandwidth of a network path to be measured in case the available bandwidths of all tight links are the same. TOPP estimates individual capacities of the congested links and estimates available bandwidth.
Pathchirp can also be applied to multiple tight links on a path while only estimating available bandwidth of that path.
Various authors have evaluated the performance comparison of available band- width estimation tools under various scenarios on a fixed IP networks [22]
[9] [3] [21]. In wired networks, available bandwidth may be defined as the unused capacity of the tight link whereas in case of mobile networks, it is not
1.4. Thesis Objective
appropriate to define as such considering varying channel resources, schedul- ing & modulation techniques, shared channel medium [7] and fading effects on the wireless networks [23].The Assumption of First In First Out (FIFO) for probe traffic in wired network is not applicable in the case of wireless mo- bile networks as the packets can compete in parallel on the network. Cross traffic available on the path can decrease the signal to noise ratio and can share common queues along the path providing wrong estimates of the avail- able bandwidth measurement. Probe packets may not be able to capture the cross traffic in the network and performance of measurement tools can be af- fected. In the case of the unused capacity being occupied by the probe traffic at that moment, higher probe traffic rate may be re-accommodated by the scheduling provided by mobile networks. Even the interference cross traffic may re-accommodate in response to the new flow. The throughput capacity of these networks is considered equal to data rate, mostly due to fluctuating radio network conditions. For poor radio channel conditions with significant bit error probability throughput decreases and hence lead to larger separation within probe packets.Various analysis of Available bandwidth estimation on 802.11 wireless networks [30] [7] [8] [50] with facing challenges [58]and mo- bile networks [15] [12] [42] have been conducted. Comparative and simulation based studies on available bandwidth estimation techniques such as TOPP, SLoPS and pathChirp over a mobile transport network has been concluded with pathChirp outperforming TOPP and SLoPS technique in the terms of the accuracy and efficiency [15]. Experiments for estimating available bandwidth have been performed over commercial UMTS channel [12] to evaluate the ap- plicability of available bandwidth measurement tools. Results show that it is possible to achieve available bandwidth estimations in certain circumstances.
In LTE, scheduling and adaptive modulation modifies the data rates where users are allotted resources according to the capacity which has dependency on current traffic demand and hence affecting throughput. With an increase in traffic rate by the user, the user may be allocated more resources with an upper bound on the limit.
The objective of the thesis is to develop a real time non-intrusive and quick two way available bandwidth probing mechanism using TWAMP. The implemen- tation should consider the QoS and resource scheduling in LTE to effectively produce accurate available bandwidth estimations.
1.4 Thesis Objective
The main objective of the thesis work is to disseminate and to connect knowl- edge from three sectors, i.e. available bandwidth measurement tools and tech- niques, Android development and LTE networks, while developing quick and non-intrusive two way available bandwidth measurement tool to measure 4G networks on Android OS based device. Here we consider the progress of In- ternet Engineering Task Force IP Performance Metric (IETF IIPM) group regarding TWAMP. The IETF is an open international community developing
1.5. Research Methodology
Internet standards. It consists of many work groups and the IP Performance Metrics work group (IIPM) [40] is the one currently releasing standards for metrics and methods used for network performance measurement.
Objectives of this research can be enlisted as follows:
• State of the art study: Literature review about available bandwidth mea- surement tools and techniques, Android development, resource schedul- ing and QoS parameters in LTE networks.
• Tool development and implementation: Select a suitable measurement technique and develop a quick and non-intrusive two way measurement tool for Android based OS considering LTE networks. It should consider minimal power and network resources, providing rich datasets regarding available bandwidth estimations and network related information.
• Application to 4G network measurement and tool verification: To con- duct real world experiments and measurements while evaluating the de- veloped measurement tool.
1.5 Research Methodology
The work has been carried over commercial 4G network testing and measure- ments.Various sections of the thesis described below were carried over with Agile development with scrum process.
ASCOM provided an problem description to estimate two way available band- width using TWAMP. Initial study on probe rate/gap model with reference materials related to BART [19] was provided. Sprint planning meetings were conducted with ASCOM employees and internal supervisor from Luleå Uni- versity of Technology to provide feedback on the thesis progress. Final presen- tation of the work was carried out at Ascom and at the university. The thesis makes following contribution which is described in the remaining chapters of the thesis.
Identify study related work, state of the art, gaps, open research and development challenges: Previous studies on comparison of various tools used in the wired and wireless network under various network scenarios was carried out. This study is unique as it provides an insight of the usefulness of the tool development in Android OS while focussing on the resource scheduling
& QoS of 4G networks.
Define, design an implement two way end-to-end available band- width estimation model: An Android OS based measurement tool and associated Java based server development implementing TWAMP is accom- plished. TWAMP logic model used has been discussed in chapter 3 with packet format usage as described in the appendix.
1.6. Individual Contribution
Verification and validation of implemented model through real world experiments and simulation: Experiments were conducted and the re- sults were achieved on a real network measurement for two way end-to-end measurement. Effect on end measurements with load on the server generated from multi-client emulator is also calculated. These overall results generated lead to conclusion, discussions and possible future work.
1.6 Individual Contribution
The initial study of related work, design test bed implementation have been done together. The measurement tool development was carried out with in- terpretation of achieved results. Whereas the measurement tool development was divided within each individual. Harpal Singh looked into the development of the Java based server and emulator and Saqlain Haider was in the role of development of Android application. Android application development was considered due to its wide popularity, being open source platform and possi- bility to look into integration of developed available bandwidth measurement tool into existing network measurement tool TEMS ™ POCKET developed by Ascom. Development in Java has been considered due to its widespread usage, ease of programming and its non dependency on the underlying OS platform.
1.7 Report Outline
The thesis work is categorized into seven chapters covering all aspects of the research development process. Chapter one provides an introduction and sum- mary to the work accomplished in the thesis. Chapter two explains about various available bandwidth estimation tools and techniques, 4G network, An- droid application development and factors affecting available bandwidth mea- surement. Chapter three covers two way metrics measurement with TWAMP to measure available bandwidth over a network path. Chapter four looks into the test bed for the TWAMP architecture implementation and the end points in the network. This forms the platform for the estimation of available bandwidth. Chapter five presents the results achieved over real network with injection of constant bit rate UDP cross traffic and load on the server. Chap- ter six discusses and concludes the master thesis from studies performed from Chapters two, three, four and five. Finally Chapter 7 indicates the future work possible in this domain.
Chapter 2
Study Review
2.1 Available bandwidth estimation tools and techniques
End-to-end available bandwidth active probing techniques as defined earlier can be categorized in two ways: The probe gap model (PGM) and the probe rate model (PRM). Looking into the PGM, it observes the packet pair disper- sion to calculate the available bandwidth whereas the PRM looks into one way delay in the probing packets.
2.1.1 Probe Gap Model
According to this model, the probing rate of the consecutive probing packets pair is set to the capacity of the tight link so that it is larger than the available bandwidth of the path. Hence the dispersion of the packet pairs is visible at the tight link. The receiver can calculate the available bandwidth by the input and the output rates of the probing packet pairs. A probe pair is sent with a time gap Din and the time calculated at the receiver end is Dout as shown in figure 2.1. With the assumption of single bottleneck and that the queue does not become empty between the departure of the first probe in a pair till the arrival of the second probe in pair, then Dout is defined as the time taken by bottleneck for the transmission of second probe in pair and the cross traffic that arrived during theDin.
Hence the time for the transmission of cross traffic is defined as Dout -Din.
Further the rate of cross traffic is defined as ( (Dout-Din)/ Din) * C where the definition of C is known capacity of the bottleneck link. To estimate the available bandwidth in the link, we determine the amount of cross traffic in the link which is further subtracted from the known capacity of the tight link.
The packet pair is sent to the path at the rate of the bottleneck link capacity C. The available bandwidth ¯A is defined as follows:
A = C ∗ (1 − )¯ (2.1.1.1)
2.1. Available bandwidth estimation tools and techniques
Where C is the known capacity of the tight link, e is strain and is defined as follows:
= ∆out − ∆in
∆in (2.1.1.2)
Hence, in PGM it is assumed that the tight link is same as the narrow link i.e
Figure 2.1: PGM Model
bottleneck and follows FIFO model. Further PGM assumes that the capacity of the link is known in advance so that it can further estimate the available bandwidth. Tools such as Spruce [52] , Abing [41] and IGI [24] are based on probe gap model.
Spruce considers Poisson technique by sending 1500-byte packet pairs as com- pared to packet trains. This makes spruce a non-intrusive and effective method.
Considering the value of the initial gap this method ensures that the bottle- neck queue does not become empty between the two probes in a pair. This technique differentiates capacity measurement from the available bandwidth measurement. Abing sends 40 back to back 1500 byte UDP packet pairs with a determined separation of 50 ms. When these pass through the tight link, probing packets can be separated by cross traffic in any place on this path.
Separation between the consecutive packets of the packet pair can happen even if there is no congestion. Hence the time delays with those probing packet pairs will be present due to the effect of the cross traffic along the path. The final time delay between these packet pairs will have information of the amount of cross traffic with different capacities. We can consider this as a load on the path. Hence time delay is proportional to the load on the path. According to this method, it consists of two components, Time delay initial (Tdinit) which is same for all measurement and this is due to the narrow link . The other to consider is Time delay variable(Tdvar) which reflects the queuing changes.
Hence final time delay between packet pairs may be defined as follows:- T d = T dinit + T dvar (2.1.1.3)
Abing is similar to other probe gap model techniques, its uniqueness lies in the
2.1. Available bandwidth estimation tools and techniques
way it makes the estimate of cross traffic passing every link in the end-to-end path to calculate the mean value for Td , available bandwidth and amount of cross traffic in the path it measures.
IGI also uses the probe gap model. Two packet pair techniques were developed by the authors to define available bandwidth. One was termed as IGI (Initial Gap Increasing) and other defined as PTR (Packet Transmission Rate). Both these algorithms send a sequence of packet trains to the destination with in- crease in initial gap. The defined algorithms monitor the difference between average Din and Dout gaps until the difference closes to zero. This is defined as the turning point after which the narrow link gets overflown by the probing packets. At this point IGI and PTR formula computes the available band- width measurement. This is obtained by subtraction of estimated competing traffic throughput from the capacity of the path measured by any capacity estimation tool. IGI operates on finding initial probing gap (gB) where the probing packet trains will interact with the cross traffic in the joint queuing region as defined by the authors. gB is defined as a gap value of two back to back probing packets on the bottleneck link.
Looking into tools based on probe gap models, initial probing packet selection can reduce the accuracy of the tool as well as other factors such as probe packet size selection and number of probe packets in a train. Small probing packets can be sensitive to interference. Further sending too many packets can cause queue overflow and packet loss. IGI suffers from accuracy in a multiple hop environment with significant cross traffic following through the tight link. It may also be unresponsive to variations in cross traffic flowing throwing through the estimation path at high speeds of Gbps.
2.1. Available bandwidth estimation tools and techniques
2.1.2 Probe Rate Model
Probe rate model works on the concept of self-induced congestion. PRM does not consider the capacity of the tight link in advance and does not assume that narrow link is same with the tight link. In this situation the probe packets are send in a train with an increasing rates and the end destination calculates the average one way delay of the train. Here we try to find the turning point as shown in figure 2.2 where the delay of the probe packets starts increasing.
Considering a train sent at a rate lower than the available bandwidth will see similar delays. If an train is sent at a rate higher than the available bandwidth the train of packets will suffer delays due to the existing cross traffic in the link and delay will be observed within the train of packets. To define the available bandwidth we probe the turning point where the one way delay initiates and the train send rate is the available bandwidth measurement of an end-to- end path.Tools like TOPP [39], Pathload [29], BART [19], pathChirp [45]and SLoPS are based on the probe rate model.
Figure 2.2: PRM model
TOPP technique sends stream of packet pairs with uniform increase in input rates in each iteration. The input rate is defined by making changes in the input gap of each pair. The available bandwidth is defined here as the esti- mation of maximum input rate which is not larger than the measurement rate at the destination end. TOPP makes an estimate to measure available band- width within a fixed range [Rmin, Rmax]. TOPP mechanism sends the packet pairs with the gradual increase in rates from the source to the destination.
Considering the initial dispersion Ds for the packet pair sent from source to the destination with probe packet of size L bytes, the rate of pair would be as follows:-
Ro = L/∆s (2.1.2.1)
2.1. Available bandwidth estimation tools and techniques
In the scenario as shown in figure 2.3 where Ro is more than the available bandwidth, second probe packet will get queued behind the first probe packet and thus the measurement rate at receiver would be Rm < Ro. For the scenario Ro< A , the packet pair arrival would be at the same rate Rm = Ro.
BART [19] stands for Bandwidth Available in Real Time which estimates end-to-end available bandwidth over a network path. BART estimates the bandwidth quasi continuously in real time. It works on the principle of self in- duced congestion where it sends sequence of probe packet pairs at randomised rates and samples the available bandwidth of the network. It generates very low probe traffic rates in a network. BART uses the concept of Kalman fil- tering for real time estimation of available bandwidth. A current estimate is maintained which is incrementally improved with each new inter packet time separation calculation in a sequence of probe packet pairs. BART can be tuned for agility vs stability of the available bandwidth estimates.
Pathload operates on SLoPS technique to measure available bandwidth. In this mechanism a stream of equally spaced packets are sent with input rate based on a binary search. This mechanism provides a range in which the available bandwidth lies rather than a single value of estimation. SLoPS works on binary search with the feedback from destination to determine the next train input rate. This methodology involves monitoring of variation in one way delays of the probing packets. In case the stream rate R is greater than the available bandwidth A of the measured path, then the stream will create a short term overload in the queue of the tight link. In case the stream rate R is lower than the available bandwidth A, the probing packets will pass through the path without overloading the queue at the link. Hence in this mechanism the sender analysis the one way delay and attempts to bring the stream rate R close to available bandwidth with iterative algorithm.To obtain network congestion we send periodic streams of packets with increasing bit rates. When the trend visibility of increasing one way delay is found to be increasing we can detect the congestion on the path and hence further analyse the available bandwidth. This technique specifies a grey region where one way delay is not clearly increasing or decreasing. It provides a range of variation of available bandwidth.
PathChirp [45] sends stream of exponentially spaced packets termed as chirps as shown in figure 2.4. Hence the instantaneous input rate changes within a single train of packets. With this mechanism only one iteration is required to get the estimation of available bandwidth as the network is probed with different input rates in each stream. The source client schedules the probing packets and the server at the other end receives the packets and analyses the queuing delay of the packets. The spread factor g sets the time gap within the two packets in a chirp. The instantaneous rate within the two consecutive packets can be defined as :
Rk= P
Tk = P
δ γk where k = 0, 1....K − 2 (2.1.2.2)
2.1. Available bandwidth estimation tools and techniques
Figure 2.3: Stream Variation with One Way Delay
here we define P as the packet size, δ as gap between the two closest packet of the stream and Tk as instantaneous gap .
In this mechanism the instantaneous rate increases along the train of packets.
To estimate the available bandwidth we analyse the queuing delay. We see an increase in queuing delay in case the instantaneous rate is higher than the available bandwidth of the network path. Here we estimate the initial instantaneous rate where we see the queuing delay. PathChirp with similarity to Pathload uses information of relative one way delay of the probing packets.
Figure 2.4: Pathchirp train structure
There lies some benefits with the use of chirps in this mechanism as they use (N-1) packets as compared to (2N-2) packets in TOPP. Further exponential spacing within packets require only log(G2)-log(G1) packets for probing the network over the range of [G1,G2] Mbps. PathChirp is able to capture delay correlation information with the use of small number of probing packets which packet pair mechanism cannot do.
2.1.3 Performance metrics
During the consideration of the tools for available bandwidth measurement in mobile networks we may look into following four factors [46] :
2.2. LTE Overview
1. Estimation Error: First metric to be considered is the estimation error.
This is estimated with comparison of the available bandwidth estimation with the real value under controlled test-bed. It is defined as a percentage error.
2. Overhead: Overhead defines the number of probe packets required by the tools to be forwarded on a network to make an estimate of avail- able bandwidth. The definition of overhead is the percentage of traffic generated by the tool with respect to the capacity of the tight link.
3. Estimation time: Estimation time can be considered as one important factor for selection of the tool. Estimation time is how long the measure- ment tool took to provide an estimate of the available bandwidth. It is usually defined in number of seconds.
4. Reliability: Reliability may be defined as the robustness of the mea- surement tool in providing the results. It is calculated by percentage of tests for which the measurement tool was able to provide available bandwidth estimation. Hence, to define a 100 % reliable measurement tool is requirement of N trials to provide N estimations.
To define an ideal tool would be the one which provides accurate estimations, less overhead, quick response time and 100 % reliability. Whereas there is no mandatory requirement of ideal tools in all scenarios. The tool selection is based on the application and the network environment.
2.2 LTE Overview
The mobile technology has evolved through times and further labelled into generations. LTE is an initiative of Third Generation Partnership Project (3GPP) release 8 which is mostly branded as 4G. However, the true evolution comes with the progress work of 3GPP release 8 termed as LTE Advanced under 3GPP release 10 . The concept merely lies here to support more devices on IP based services. The whole LTE network is based around packet switched services and was primarily developed to provide higher data rate services, lower delay for interactive services and higher spectral efficiency to the user.
3GPP is the standard developing body that looks into the LTE /LTE-Advanced specifications. It was also responsible for the standards of 3G UTRA and 2G GSM systems. This group further has Technical Specifications Groups (TSGs) that looks into various sub parts of the network architecture. Considering 3GPP, LTE Advanced may be defined as a major evolution in mobile technol- ogy which will have a smooth transition from the current LTE release 8. LTE supports peak data rates of 100 Mbps for downlink and 50 Mbps for uplink traffic. The advanced antenna techniques and adaptive modulation & coding with the usage of Orthogonal Frequency Division Multiple Access (OFDMA) help LTE achieve significant throughput and spectral efficiency enhancement.
2.2. LTE Overview
Operators can have the benefit to transfer more data per MHz of spectrum which allows them to have lower cost per bit. The distribution of functions for radio access network between 3G RNC and NodeB is now simplified in LTE with base station or evolved-NodeB (eNodeB). The eNodeB MAC sub layer is responsible for scheduling transmission over both uplink and downlink. LTE deploys OFDM and SC-FDMA for downlink and uplink transmission respec- tively. OFDM allows support for both time and Frequency duplexing modes (TDD & FDD). It relies on the rapid adaptation to channel conditions and employs rate adaptation and hybrid soft combining techniques.
LTE architecture can be split into two parts, a radio access network and a core network. Radio access networks is named as E-UTRAN and handles modula- tion, compression and handover. The core network is named as Evolved Packet Core(EPC) which looks into functions like charging and mobility management.
The EPC consists of Mobility Management Entity (MME), Serving Gateway, Packet Data Network gateway (PDN) and Policy & Charging Rules Function (PCRF). MME handles control plane functions which are related to subscriber and session management. Serving Gateway is a routing node for other 3GPP technologies and for packet flows towards E-UTRAN. PDN Gateway provides the access to the external network and acts as a router. PCRF manages IP Multimedia Subsystem(IMS) configuration of each subscriber and manages the traffic flow.
Figure 2.5: LTE frame structure
2.2. LTE Overview
For data transmission LTE uses a frame of 10ms in length divided into 10 subframes in time domain of each 1 ms in length as shown in figure 2.5.
A subframe is divided into 2 slots in the time domain of 0.5 ms in length.
Each of this slot is divided into number of resource blocks in the slot. Each resource block is 0.5 ms in length containing 12 sub-carriers from each of the OFDM frequency domain. The channel bandwidth ranging from 1.25 MHz till up to 20 MHz of LTE network defines the number of resource blocks in symbol. The cyclic prefix being used defines the number of OFDM symbols in a resource block. Group of resource blocks make a transport block having common modulation/coding. To schedule transmission over an air interface resource block is considered the main unit. Multiple UE’s can be addressed in a single transport block where the MAC has the control of what to send at particular time. Using OFDMA, users are allocated specific numbers of physical resource blocks (PRBs) by the scheduler at eNodeB. Shared channel transmission is utilized in LTE transmission scheme where the resources are dynamically shared within the user terminals.
2.2.1 LTE Radio Interface Architecture
The LTE protocol stack consists of Medium Access Control(MAC), Radio Link Control(RLC), Packet Data Convergence Protocol (PDCP) and Radio Resource Control(RRC). The data on the downlink channel is transmitted within the processing chain in the form of IP packets on one of the bearers over the radio link. PDCP performs IP header compression, ciphering and integrity protection of transmitted data of the radio interface. On the receiver side it performs deciphering and decompression. RLC is responsible for seg- mentation/concatenation, retransmission handling and in sequence delivery to higher layer in protocol stack located in eNodeB. MAC handles uplink and downlink scheduling, logical channel multiplexing as well as hybrid-ARQ re- transmissions. The scheduling is located in eNodeB while the hybrid-ARQ protocol is present in both eNodeB and the terminal. The MAC provides services to RLC in the form of logical channels. A channel is defined on the basis of type of information carried by it, classified as either control channel utilized for information regarding transmission of control and configuration for functioning of LTE systems or traffic channel for user data. Physical Layer (PHY) handles coding/decoding, modulation and demodulation. It also han- dles multi-antenna mapping. The physical layer provides services to MAC layer in the form of transport channels. Definition of transport channel may be considered as how and with what kind of characteristics, the information has been transmitted over radio interface in LTE system. DL-SCH is the main channel for data transmission in the downlink. On the control plane RRC handles radio bearer set up, broadcasting of system information and active mobility management. NAS protocol is assigned a functionality to deal with idle mode mobility management and for the service set up.
Scheduler located at eNodeB is defined as the part of the MAC layer being
2.2. LTE Overview
responsible for scheduling of shared radio resource blocks for transmission over the LTE air interface in both uplink and downlink channel and it also deter- mines the data rate for each link. The data is transferred between the MAC sub-layers in UE and eNodeB with the usage of constructed transport blocks.
Downlink and uplink shared transport channels (DL-SCH and UL-SCH) are used for sending these transport blocks. In LTE, the scheduling decision is possible at once very 1ms with the granularity in frequency domain being 180 kHz.Various scheduling algorithms are implemented for resource block al- location such as Proportional Fair (PF), Maximum Throughput(MT), Round Robin, Blind Equal Throughput (BET) or Even Resources (ER).
The downlink scheduler dynamically controls the terminal(s) to transmit to.
It controls the resource blocks upon which the user terminal’s DL-SCH should be transmitted. The uplink scheduler dynamically controls the mobile ter- minals who are ready to transmit on their UL-SCH and also defines which uplink time/frequency resources are available. The uplink scheduling occurs per mobile terminal and not per radio bearer. The channel-status reports indi- cating the instantaneous channel quality both in time and frequency domains is sent by mobile terminals for channel dependent scheduling typically for down- link. A mobile terminal can also send buffer status information to eNodeB using a MAC message to assist the uplink scheduler in decision making.The channel-status report consists of Rank Indication (RI), Precoder Matrix Indi- cation(PMI), Channel Quality Indication(CQI). The reporting can be either periodic or a-periodic. Channel dependent scheduling allows to achieve gain in system capacity.
Scheduling grants, if available to terminal forms the basis of scheduling decision and provides the user terminal information about the resources available and the associated transport format to use for the transmission on the UL-SCH.
Similar to the downlink scheduler, the uplink scheduler can retrieve informa- tion regarding the buffer status, channel conditions and priorities of different traffic flows. A scheduling request is defined as a flag raised by the terminal for the request of uplink resources from uplink scheduler. On the reception of the request the scheduler allots grant to the terminal. The scheduler utilizes the following input for scheduling decision.
• Radio conditions existing at the UE side
• Buffer status reports
• QoS attributes
• Interference situation in neighbouring cells
2.2.2 QoS Control in LTE
QoS as standardized in 3GPP release 8 provides network operators and service providers with a set of tools to initiate service and subscriber differentiation.
2.2. LTE Overview
It allows them to offer multiple services to the end users. Subscriber differenti- ation allows service providers to differentiate subscribers for the same service.
It may be such as post or prepaid connections, corporate or private subscribers and roaming services. LTE utilizes various techniques for radio resource man- agement to maximize cell throughput while maintaining QoS and fairness for users and the offered services. At a particular location, to analyse the QoS experienced by the end user the measurements statistics are averaged over a period of time. This may actually provide errors on the estimation. Drive testing is the means of collection of data related to radio interface in different locations of the cellular network coverage. A lot of information regarding the signal strength, user throughput, delays due to traffic distribution and infor- mation about call drops are analysed. Data collection can be categorized as Instantaneous data collection and time averaged data collection. Instantaneous data collection collects log information of throughputs values within the resolu- tion of 1ms as for the Transmission Time Interval (TTI) providing information for peak throughputs. Whereas these values are dependent on the resource al- location and scheduling algorithm at the instant in the network. Hence Time averaged data collection is based on time averaged sample values which are smoothed for giving information for per TTI basis. These measurements for various locations allow us to make information network maps.
Various radio measurements provide information on the QoS for the end user.
Main information is the Reference Signal Received Power (RSRP). It is essen- tial for handover and cell selection procedure. RSRP which is a pilot mea- surement, defined as a linear average over the power contributions (in W) of the resource elements that carry cell-specific reference signals within the considered measurement frequency band. Another radio measurement of user throughput is Channel Quality Indicator (CQI) or the averaged CQI value for the whole system bandwidth known as Wideband CQI. LTE defines 16 CQI levels for different Modulation and Coding Schemes (MCS). WCQI can assist us an indication for data rate supported by the system for the given channel conditions. The bitrate per symbol rate obtainable in an LTE network depends on the SINR for both the links. As discussed earlier CQI is signalled over the radio interface which determines the coding rate based on modulation such as QPSK, 16QAM, 64QAM and the amount of redundancy.
The bearer which is short for EPS bearer may be considered as the core element of QoS in LTE. It identifies the packet flows between terminal and gateway that receive a common QoS treatment as shown in figure 2.6. The packet filters in terminal and gateway for uplink and downlink traffic respectively determines the packet flows association with the bearer. Hence the packet flow which is mapped to same bearer receives the same packet forwarding treatment(for example, scheduling policy, queue management policy, rate shaping policy, link layer configuration). Separate bearers are required for different packet forwarding treatment. This allow differentiation of traffic in LTE with different QoS requirements. The system reserves the resources according to the bearer and associated signalling procedures before the transmission on packet flows
2.2. LTE Overview
Figure 2.6: The bearer and its associated QoS parameters
over the network with the assistance of admission control function. There is one bearer for each combination of QoS class and IP address of the user terminal and different ones for a terminal with different Access Point Names (APN).
For an end-to-end IP system, a tunnel header is attached which contains the bearer identification allowing the network nodes to associate the packet with correct QoS parameters. Classification of bearers can be done as follows:
GBR vs Non GBR Bearers: We can differentiate bearers in two kinds:
Guaranteed bit rate(GBR) and non guaranteed bit rate(non-GBR). Services utilizing GBR do not confer congestion related packet loss whereas for services with non-GBR can experience congestion related packet loss and are realized by the admission control function. A GBR bearer reserves transmission resources for itself in an admission control system when established. Whereas non-GBR does not block transmission resources and remain established for long period of time. An operators can define services with GBR bearers to provide better user experience.
Default versus Dedicated bearers: A bearer can also be defined as a de- fault bearer or an dedicated bearer. When an terminal attaches to a network it is connected via a default bearer being associated with per terminal IP ad- dress till existence of the network connection. The default bearer is considered as a non-GBR bearer and its associated QoS is based on subscription data.
Different Dedicated bearers are required for packet flows flowing from same
2.2. LTE Overview
IP address of the terminal with different QoS parameters. A dedicated bearer can be defined as a GBR bearer or an non-GBR bearer. PCRF looks into QoS levels of the dedicated bearers. It defines mapping of specific packet flows to dedicate bearers. For packets not associated with existing dedicated bearers or drop of dedicated bearers, they are mapped to default bearers.
QoS Parameters
The QoS concept in EPS is class based where each of the bearer is assigned only one QoS Class Identifier (QCI) by the network.
QCI is pre-configured by operators and is defined as a scaler used with the access network as a reference to the node specific parameters which control packet forwarding treatment on user plane. QCI standardization is to ensure that the application or services mapped to the QCI receive similar minimum level of QoS in multi-vendor network deployment and roaming services.
Allocation and retention priority(ARP) provides control plane treatment re- lated to the set up and retention of the bearers and decide which bearer to be released during resource limitations.
Maximum Bit Rate(MBR)and Guaranteed Bit Rate(GBR) are defined for GBR bearers only. MBR is the bit rate that the bearer may not exceed. Whereas GBR is the bit rate the network guarantees using admission control function.
Set up of MBR higher than GBR is set for future releases of 3GPP.
Aggregate Maximum Bit Rate (AMBR) allows operators to limit the total amount of bit rate consumption by the subscriber and offer differentiated sub- scriptions. 3GPP specifies two different AMBR parameters:
• APN-AMBR: This is definition for per subscriber and APN and its knowledge is only to the gateway
• Terminal-AMBR: This is definition for per subscriber and its knowledge is with gateway and RAN.
The AMBR definition is for the non-GBR bearers only and its different for both the uplink and downlink with four definitions as UL APN-AMR, UL terminal-AMBR. DL APN-AMR and DL terminal-AMBR.
QoS mechanism:
QoS mechanism in EPS can be defined as user plane functions or control-plane signaling procedures.
Control-plane signalling Procedures: PCRF can issue policy and charging con- trol (PCC) rules for the gateway. This can be used for new bearer estab- lishment or modification of existing bearers which handles the packet flow.
UL/DL packet filters describe the packet flow.
2.2. LTE Overview
User Plane functions: User plane QoS functions are carried out by configura- tion of network nodes on packet-flow-level functions, bearer-level functions or DSCP-level functions.
• Packet-flow-level functions allows network node to manage the bit rate assigned to the subscriber such as flat rate pricing plan.
• Bearer-level functions: Uplink and downlink packet filtering occurs at terminal and gateway for mapping of packet flows onto intended bearers.
Limitation and control of the nodes by admission control and conges- tion control can be implemented by the gateway and the RAN. The rate policing also occurs at gateway and the RAN to protect network from overloading and service assurance. Uplink and downlink scheduling function is implemented by the LTE RAN to distribute Radio resources which looks into fulfilment of QoS characteristics of bearers as well as configuration of L1 & L2 protocols and error control protocols. QCI to DSCP mapping function looks into traffic separation in transport net- work for the transport form bearer level QoS (QCI) to transport level QoS (DSCP). Gateway performs the mapping for downlink packets and LTE RAN maps the uplink packets.
• DSCP-level functions:The transport network forwards each individual packet based on the DSCP value with the implementation of the queue management scheme and scheduling algorithm for both the uplink and downlink traffic .
Network and Terminal initiated QoS Control
Dedicated bearer with a specific QoS can be established either by terminal initiated or network initiated QoS control paradigms. In the network initi- ated QoS control, the network looks into the initiation of signal which sets up the dedicated bearer having specific QoS towards the terminal and RAN.
The client application is not required to be aware of QoS specification of the access network although it has knowledge of the QoS. In a terminal initiated QoS control paradigm, the signal to set up a dedicated bearer associated with specific QoS is initiated by the terminal. The client application on the ter- minal needs to be aware of the QoS model specifications of access networks to specify the QoS information for the bearer. Network initiated QoS control had advantages over terminal initiated QoS control to minimize the terminal in QoS and policy control.
2.3. Android OS
2.3 Android OS
Google’s Android OS has gained a high market presence which includes mid- dleware, key applications and software stack for the mobile devices. Android OS is based on the modified version of Linux Kernel version 2.6 which sup- ports core systems as security, memory management, drivers, process man- agement and network stack. Google and the other members of Open handset Alliance [4] have collaborated for Android’s development and release where as Android Open Source Project (AOSP) [5] is the one for maintenance an further development of Android. Open handset alliance is a group of various hard- ware, software and telecom companies looking into the advancement of open standards for mobile devices. The android OS distribution was released on 5 November 2007 with the foundation of Open Handset Alliance. The Android code was released under the Apache License, a free software and open source license. Android OS platform can be subdivide into following 5 layers as shown in figure 2.7:
Figure 2.7: Android OS Layer
1. Application: Some of the core applications are on the top of the frame- work which includes such as e-mail client, SMS app, Maps application, Web browser, Contacts etc.
2. Application Framework: Application Framework acts a base for develop- ment of applications in Android. The main components within the Ap- plication Framework are the Activity manager, Window manager, Con- tent providers, View system, Notification manager, Package manager, Telephony manager, Resource Manager, Location Manager and XMPP service.
2.3. Android OS
3. Libraries: Android contains a set of C/C++ libraries which are used by various components of the Android System. The main core libraries de- fine are Media framework, WebKit, SGL, OpenGL ES, FreeType, SQLite etc. The developers can access them though the Android Application Framework.
4. Android Runtime: Android OS includes a set of core libraries which provide most of the functionality available in the core libraries of the Java programming language. For every Android application it runs its own instance of the Dalvik Virtual Machine. Parallel Virtual machines can run in Dalvik. The .dex(Dalvik Executable) format are executed in the Dalvik VM which is optimized for minimal CPU and memory usage.
5. Linux Kernel: Android operates on Linux Kernel version 2.6 for the core system services. They include memory management, process manage- ment, network stack, security and driver model. It also acts as a hard- ware abstraction layer between the application and all the hardware.
Memory management is essential in smart phones due to memory constraints in them. Hence it becomes essential to de-allocate memory allotted to objects that are no longer required. This can be either taken care by the system or the developer managing the memory allotment in the program code. Dalvik’s Garbage Collector(GC) automatically provides the memory management sup- port. Each process running on an Android device uses separate GC instance and these collectors do not interfere with the instances of the other running applications. Dalvik employs a type known as tracing GC and it utilizes mark and sweep approach. In this type, the initial step is where the collector keeps mark bits to indicate that a specific object is reachable and hence should not be garbage collected. At the second phase, all the objects that had been marked reachable are garbage collected. This algorithm comes with the advantage to identify and collect garbage even in the presence of reference cycles. On the other hand it has a disadvantage where the program execution must be halted to run the algorithm. Android is a asynchronous system where multi- ple events can be bound to multiple operations. Intents/Notifications/ Signals can be considered to trigger life cycle state change. Each application launched runs in its own Linux process with its own VM. Each application has its own Linux user ID associated to it and the permissions set for the application is just visible to that specific user or the application itself. Combination of both mechanisms creates a sandbox which then prevents one application to interrupt an other.
2.3.1 Android supported API’s
Android provides 3 sets of API namely SDK [27] with Java Support, NDK [25]
with native support and Renderscript support [26].
2.3. Android OS
Figure 2.8: Android SDK flow
Figure 2.9: Android NDK flow
2.3. Android OS
Android Software Development Kit(SDK) provides necessary tools and libraries required by the programmers to create applications to be run on an Android device using Java. The first SDK version was released on 12 December 2007 whereas the latest version currently released is SDK Tools, Revision 20.0.1 (July 2012). Java allows easy development of applications and it can be portable to various platforms. Java has its own advantages such as language level security and portability of programs developed in SDK being easily run on different Android devices. The source code of the applications developed using SDK is complied to bytecode on a developer machine as shown in fig- ure 2.8. On the launch of the application developed, DalvikVM as an android execution engine interprets the bytecode or it uses the JIT compiler for the compilation of byte code into machine instructions and hence execute it.
Android Native Development Kit(NDK) is a tool set which provides usage of native code in our Android application. Hence it allows the developers to create and compile code in C/C++ for an Android platform. The first NDK version was released on June 2009 and the latest version currently released is Android NDK, Revision 8 (May 2012). Android NDK allows us to include native code for performance critical parts of the application and the reuse of code written in C/C++ language. In Android NDK as shown in figure 2.9, the code developed is compiled into target machine code and it is then packaged into .apk execution file. It has to be used with java code in order to run in Android device. The native code developed is executed through the usage of Java Native Interface(JNI) as shown in figure 2.11. Usage of native code is restricted to use functionalities provided by Android. Hence usage of JNI allows access to all functionalities through SDK API from the native code. For NDK applications developed to be able to run over various CPU architecture, the developer needs to develop and built different versions of native library for target application binary interface.
Renderscript has been introduced since release of Android 3.0 to provide so- lutions to performance problems seen in SDK with Java and also the porta- bility issues with NDK with C/C++. It provides API for the functionality and graphics on an Android device. C99 is adapted in Renderscript as the base programming language. It operates close to the target architecture. It has features of C language like flexibility for data manipulation and portabil- ity over various android platforms. Whereas Renderscript application cannot function alone without the usage of Android SDK code. The source code written in Renderscript is complied by C99 frontend compiler slang with two targets i.e. LLVM bitcode as the intermediate representation of the program and with reflection Java class as glue layer between Android SDK Java code and Renderscript code as shown in figure 2.10. SDK code uses the reflection Java code to invoke Renderscript function, write Renderscript variables and manage Renderscript memory allocation. The API for the computes part is a subset of C99, while it adds vector type which allows to facilitate the array or matrix computation. The graphics part in Renderscript is a wrapper of OpenGL ES2.0.
